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Build sophisticated multi-agent AI systems where specialised agents collaborate to complete complex business tasks using StackOne’s infrastructure of thousands of turnkey tools, RPC orchestration, and MCP/A2A entry points.
CrewAI may have compatibility issues with StackOne’s LangChain tools depending on your CrewAI and pydantic versions. If you encounter a ValidationError for BaseTool, check for version updates or use the OpenAI integration instead.

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Overview

  • Multi-agent collaboration with business data access
  • Specialized agents for different business functions
  • Complex workflow automation with task dependencies
  • Enhanced error handling and monitoring
from crewai import Agent, Crew, Task
from stackone_ai import StackOneToolSet

def create_hr_crew(account_id: str):
    """
    Create multi-agent crew with tools for a specific account.

    In production, account_id comes from:
    - User/tenant context
    - Authentication middleware
    - Database lookup
    """
    # Initialize toolset
    toolset = StackOneToolSet()

    # Fetch tools dynamically for this account
    tools = toolset.fetch_tools(
        account_ids=[account_id]
    ).to_langchain()

    # Optional: Filter by operation type for security
    # read_only_tools = toolset.fetch_tools(
    #     account_ids=[account_id],
    #     actions=["*_list_*", "*_get_*"]  # Only list and get operations
    # ).to_langchain()

    # Create specialized agents
    hr_manager = Agent(
        role="HR Manager",
        goal="Manage employee data and handle HR requests",
        backstory="Expert in human resources with experience in employee management",
        tools=tools,
        llm="gpt-5.4",
        verbose=True
    )

    recruiter = Agent(
        role="Recruiter",
        goal="Find and evaluate candidates for open positions",
        backstory="Experienced recruiter with talent acquisition expertise",
        tools=tools,
        llm="gpt-5.4",
        verbose=True
    )

    # Create collaborative tasks
    employee_analysis_task = Task(
        description="Analyze current employee distribution by department",
        agent=hr_manager,
        expected_output="Employee distribution report with staffing recommendations"
    )

    # Execute multi-agent crew
    crew = Crew(
        agents=[hr_manager, recruiter],
        tasks=[employee_analysis_task],
        verbose=True
    )

    return crew

# Usage: Get account from user context
# Get account ID from your app's auth context or StackOne dashboard
account_id = "your-account-id"
crew = create_hr_crew(account_id)
result = crew.kickoff()
print(result)

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Example